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D-Index & Metrics

Computer Science

D-Index
53
Citations
17325
World Ranking
4707
National Ranking
188

Research.com Recognitions

  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering

Overview

Kevin Englehart is affiliated with the University of New Brunswick in Canada. Their research primarily focuses on engineering and neuroscience, with a significant emphasis on biomedical engineering and cognitive neuroscience. They have also contributed to fields related to physical therapy, sports therapy, rehabilitation, cellular and molecular neuroscience, and social psychology.

The main topics of their work include muscle activation and electromyography studies, motor control and adaptation, balance, gait, and falls prevention, as well as advanced sensor and energy harvesting materials. Their research also covers neuroscience and neural engineering, EEG and brain-computer interfaces, and ergonomics and musculoskeletal disorders.

Kevin Englehart has published articles in several scientific venues with a notable presence in the IEEE Transactions on Neural Systems and Rehabilitation Engineering and other journals such as Sensors, Scientific Reports, and bioRxiv (Cold Spring Harbor Laboratory).

  • A Multi-Variate Approach to Predicting Myoelectric Control Usability, 2021, IEEE Transactions on Neural Systems and Rehabilitation Engineering
  • A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN, 2020, Sensors
  • Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions, 2024, IEEE Transactions on Neural Systems and Rehabilitation Engineering
  • An analytical method reduces noise bias in motor adaptation analysis, 2021, Scientific Reports
  • Cutting through the noise: reducing bias in motor adaptation analysis, 2020, bioRxiv (Cold Spring Harbor Laboratory)

Englehart has collaborated frequently with several coauthors, including Erik Scheme, Daniel Blustein, Ahmed W. Shehata, Erin S. Kuylenstierna, and Jonathon W. Sensinger, each contributing to multiple projects alongside them.

They have been recognized by The Canadian Academy of Engineering, an acknowledgment affiliated with their professional standing in the engineering community.

Best Publications

  • A robust, real-time control scheme for multifunction myoelectric control

    K. Englehart;B. Hudgins

  • Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms.

    Todd A. Kuiken;Guanglin Li;Blair A. Lock;Robert D. Lipschutz

  • Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use.

    Erik Scheme;Kevin Englehart

  • A wavelet-based continuous classification scheme for multifunction myoelectric control

    K. Englehart;B. Hudgin;P.A. Parker

  • Classification of the myoelectric signal using time-frequency based representations

    K Englehart;B Hudgins;P.A Parker;M Stevenson

  • A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses

    Yonghong Huang;K.B. Englehart;B. Hudgins;A.D.C. Chan

  • Myoelectric signal processing for control of powered limb prostheses.

    P. Parker;K. Englehart;B. Hudgins

  • A Comparison of Surface and Intramuscular Myoelectric Signal Classification

    L.J. Hargrove;K. Englehart;B. Hudgins

  • Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion

    He Huang;Fan Zhang;L. J. Hargrove;Zhi Dou

  • Extracting Simultaneous and Proportional Neural Control Information for Multiple-DOF Prostheses From the Surface Electromyographic Signal

    Ning Jiang;K.B. Englehart;P.A. Parker

  • Resolving the Limb Position Effect in Myoelectric Pattern Recognition

    A. Fougner;E. Scheme;A. D. C. Chan;K. Englehart

  • Continuous myoelectric control for powered prostheses using hidden Markov models

    A.D.C. Chan;K.B. Englehart

  • A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control

    Levi J. Hargrove;Kevin B. Englehart;Bernard Hudgins

  • Simultaneous and Proportional Force Estimation for Multifunction Myoelectric Prostheses Using Mirrored Bilateral Training

    Johnny L G Nielsen;S Holmgaard;Ning Jiang;K B Englehart

  • Principal Components Analysis Preprocessing for Improved Classification Accuracies in Pattern-Recognition-Based Myoelectric Control

    L.J. Hargrove;Guanglin Li;K.B. Englehart;B.S. Hudgins

  • Decoding a new neural-machine interface for control of artificial limbs

    Ping Zhou;Madeleine M. Lowery;Madeleine M. Lowery;Kevin B Englehart;He Huang

  • Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis

    L.J. Hargrove;E.J. Scheme;K.B. Englehart;B.S. Hudgins

  • Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography

    Claudio Castellini;Panagiotis K. Artemiadis;Michael Wininger;Arash Ajoudani

  • Selective Classification for Improved Robustness of Myoelectric Control Under Nonideal Conditions

    E J Scheme;K B Englehart;B S Hudgins

  • Support Vector Regression for Improved Real-Time, Simultaneous Myoelectric Control

    Ali Ameri;Ernest N. Kamavuako;Erik J. Scheme;Kevin B. Englehart

Frequent Co-Authors

Erik Scheme
Erik Scheme University of New Brunswick
Philip A. Parker
Philip A. Parker University of New Brunswick
Levi J. Hargrove
Levi J. Hargrove Northwestern University
Ning Jiang
Ning Jiang University of Waterloo
Dario Farina
Dario Farina Imperial College London
Todd A. Kuiken
Todd A. Kuiken Northwestern University
Guanglin Li
Guanglin Li Chinese Academy of Sciences
Antonio Bicchi
Antonio Bicchi Italian Institute of Technology
Muhammad Shafique
Muhammad Shafique New York University Abu Dhabi
Strahinja Dosen
Strahinja Dosen Aalborg University

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